Análise - Doutorado - Edvaldo Aldo Litos Paulo Nhanombe
Pré-tratamento dos dados
dados_batata_v <- readxl::read_xlsx("../data-raw/Exp. Verão-2024_25.xlsx") |>
janitor::clean_names()
readr::write_rds(dados_batata_v,"../data/batata-doce-ednaldo-v.rds")
dados_multi_v <- readxl::read_xlsx("../data-raw/HTP_Verão_epocas_final.xlsx") |>
janitor::clean_names()
readr::write_rds(dados_multi_v,"../data/batata-doce-multiespectral-v.rds")
dados_batata_i <- readxl::read_xlsx("../data-raw/Exp. Inverno-2024_25.xlsx") |>
janitor::clean_names()
readr::write_rds(dados_batata_i,"../data/batata-doce-ednaldo-i.rds")
dados_multi_i <- readxl::read_xlsx("../data-raw/HTP_inverno_epocas_final.xlsx") |>
janitor::clean_names()
readr::write_rds(dados_multi_i,"../data/batata-doce-multiespectral-i.rds")Análise exploratória - Batata Doce - INVERNO
Carregando pacotes e banco de dados
library(tidyverse)
data_set <- read_rds("../data/batata-doce-ednaldo-i.rds")lista_variaveis <- data_set |> select(pt:tmspa_percent) |> names()
# map(lista_variaveis, ~{
# data_set |>
# ggplot(aes(x=!!sym(.x), y = ..density..)) +
# geom_histogram(color="black",fill="gray", bins = 15) +
# labs(title = .x) +
# theme_bw()
# })Análise de resÃduos - Pré-supostos da ANOVA
map(lista_variaveis, ~{
print("========================")
print(.x)
print("========================")
y <- data_set |> pull(!!sym(.x))
trat <- data_set |> pull(designacao) |> as_factor()
bloco <- data_set |> pull(bloco) |> as_factor()
mod <- aov(y ~ trat + bloco)
print(anova(mod))
rs <- rstudent(mod)
yp <- predict(mod)
sw_test <- shapiro.test(rs)
sw_test <- round(sw_test$p.value,5)
print(
as_tibble(rs) |>
ggplot(aes(rs)) +
geom_histogram(bins=14,color="black",fill="aquamarine4") +
labs(title = .x,
subtitle = paste("Shapiro-Wilk - p-valor: ",sw_test)) +
theme_bw()
)
df_aux <- data_set |>
select(designacao, bloco,!!sym(.x)) |>
add_column(rs,yp) |>
filter(rs > 3 | rs < -3)
# arrange(rs)
if(nrow(df_aux) != 0) print(df_aux)
levene_teste <- lawstat::levene.test(y,trat)
levene_teste <- round(levene_teste$p.value,5)
box_plot <- data_set |>
group_by(designacao) |>
mutate(
y_mean = median(!!sym(.x),na.rm=TRUE),
designacao = as_factor(designacao)) |>
ungroup() |>
mutate(designacao = designacao |> fct_reorder(y_mean)) |>
ggplot(aes(x=as_factor(designacao),y=!!sym(.x),
fill=as_factor(designacao))) +
geom_boxplot() +
scale_fill_viridis_d(option = "magma") +
theme_bw()+
labs(x="Designacao",
title = paste("Levene test - p-valor: ",levene_teste))
print(
box_plot
)
print(cat("\n"))
})
#> [1] "========================"
#> [1] "pt"
#> [1] "========================"
#> Analysis of Variance Table
#>
#> Response: y
#> Df Sum Sq Mean Sq F value Pr(>F)
#> trat 17 9.1572 0.53866 3.2652 0.001617 **
#> bloco 2 0.7532 0.37658 2.2827 0.117427
#> Residuals 34 5.6090 0.16497
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#> # A tibble: 2 × 5
#> designacao bloco pt rs yp
#> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 14 1 2.52 3.25 1.60
#> 2 14 3 0.531 -3.09 1.42
#>
#> NULL
#> [1] "========================"
#> [1] "pc"
#> [1] "========================"
#> Analysis of Variance Table
#>
#> Response: y
#> Df Sum Sq Mean Sq F value Pr(>F)
#> trat 17 9.2504 0.54414 3.8296 0.0004222 ***
#> bloco 2 0.7344 0.36720 2.5843 0.0901973 .
#> Residuals 34 4.8310 0.14209
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#> # A tibble: 2 × 5
#> designacao bloco pc rs yp
#> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 14 1 2.50 3.91 1.52
#> 2 14 3 0.481 -3.72 1.43
#>
#> NULL
#> [1] "========================"
#> [1] "pnc"
#> [1] "========================"
#> Analysis of Variance Table
#>
#> Response: y
#> Df Sum Sq Mean Sq F value Pr(>F)
#> trat 17 0.28610 0.016829 0.8721 0.6076
#> bloco 2 0.08263 0.041316 2.1409 0.1331
#> Residuals 34 0.65615 0.019298
#> # A tibble: 2 × 5
#> designacao bloco pnc rs yp
#> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 8 1 0.662 3.65 0.318
#> 2 10 1 0.811 5.39 0.371
#>
#> NULL
#> [1] "========================"
#> [1] "nrt"
#> [1] "========================"
#> Analysis of Variance Table
#>
#> Response: y
#> Df Sum Sq Mean Sq F value Pr(>F)
#> trat 17 95.367 5.6098 2.7879 0.00534 **
#> bloco 2 3.586 1.7928 0.8910 0.41961
#> Residuals 34 68.414 2.0122
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#> # A tibble: 2 × 5
#> designacao bloco nrt rs yp
#> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 3 1 4.5 -3.10 7.62
#> 2 3 3 11.5 4.63 7.38
#>
#> NULL
#> [1] "========================"
#> [1] "nrc"
#> [1] "========================"
#> Analysis of Variance Table
#>
#> Response: y
#> Df Sum Sq Mean Sq F value Pr(>F)
#> trat 17 63.260 3.7212 4.4458 0.0001065 ***
#> bloco 2 4.333 2.1667 2.5886 0.0898631 .
#> Residuals 34 28.458 0.8370
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#>
#> NULL
#> [1] "========================"
#> [1] "nrnc"
#> [1] "========================"
#> Analysis of Variance Table
#>
#> Response: y
#> Df Sum Sq Mean Sq F value Pr(>F)
#> trat 17 9.2176 0.54221 0.9848 0.4957
#> bloco 2 1.5301 0.76505 1.3895 0.2630
#> Residuals 34 18.7199 0.55059
#> # A tibble: 1 × 5
#> designacao bloco nrnc rs yp
#> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 3 3 3.75 5.02 1.49
#>
#> NULL
#> [1] "========================"
#> [1] "msr_100_g"
#> [1] "========================"
#> Analysis of Variance Table
#>
#> Response: y
#> Df Sum Sq Mean Sq F value Pr(>F)
#> trat 17 0.00054259 3.1917e-05 7.5613 3.351e-07 ***
#> bloco 2 0.00007315 3.6574e-05 8.6645 0.0009098 ***
#> Residuals 34 0.00014352 4.2210e-06
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#> # A tibble: 1 × 5
#> designacao bloco msr_100_g rs yp
#> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 13 1 0.025 -3.20 0.0296
#>
#> NULL
#> [1] "========================"
#> [1] "mr"
#> [1] "========================"
#> Analysis of Variance Table
#>
#> Response: y
#> Df Sum Sq Mean Sq F value Pr(>F)
#> trat 17 0.66274 0.038985 4.2389 0.0001675 ***
#> bloco 2 0.01085 0.005427 0.5901 0.5598679
#> Residuals 34 0.31270 0.009197
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#> # A tibble: 2 × 5
#> designacao bloco mr rs yp
#> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 14 1 0.841 5.21 0.543
#> 2 14 3 0.236 -4.53 0.511
#>
#> NULL
#> [1] "========================"
#> [1] "mrc"
#> [1] "========================"
#> Analysis of Variance Table
#>
#> Response: y
#> Df Sum Sq Mean Sq F value Pr(>F)
#> trat 17 0.79913 0.047007 5.1261 2.58e-05 ***
#> bloco 2 0.02062 0.010309 1.1241 0.3367
#> Residuals 34 0.31179 0.009170
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#> # A tibble: 2 × 5
#> designacao bloco mrc rs yp
#> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 14 1 0.908 6.32 0.580
#> 2 14 3 0.275 -4.25 0.539
#>
#> NULL
#> [1] "========================"
#> [1] "tms_percent"
#> [1] "========================"
#> Analysis of Variance Table
#>
#> Response: y
#> Df Sum Sq Mean Sq F value Pr(>F)
#> trat 17 542.59 31.917 7.5613 3.351e-07 ***
#> bloco 2 73.15 36.574 8.6645 0.0009098 ***
#> Residuals 34 143.52 4.221
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#> # A tibble: 1 × 5
#> designacao bloco tms_percent rs yp
#> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 13 1 25 -3.20 29.6
#>
#> NULL
#> [1] "========================"
#> [1] "ptms"
#> [1] "========================"
#> Analysis of Variance Table
#>
#> Response: y
#> Df Sum Sq Mean Sq F value Pr(>F)
#> trat 17 0.49376 0.029045 3.5783 0.0007603 ***
#> bloco 2 0.07580 0.037902 4.6695 0.0161506 *
#> Residuals 34 0.27598 0.008117
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#> # A tibble: 1 × 5
#> designacao bloco ptms rs yp
#> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 14 3 0.106 -3.00 0.299
#>
#> NULL
#> [1] "========================"
#> [1] "ptms_kg_ha"
#> [1] "========================"
#> Analysis of Variance Table
#>
#> Response: y
#> Df Sum Sq Mean Sq F value Pr(>F)
#> trat 17 44276548 2604503 3.5783 0.0007603 ***
#> bloco 2 6797527 3398764 4.6695 0.0161506 *
#> Residuals 34 24747226 727860
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#> # A tibble: 1 × 5
#> designacao bloco ptms_kg_ha rs yp
#> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 14 3 1006. -3.00 2835.
#>
#> NULL
#> [1] "========================"
#> [1] "mfpa_kg_pl"
#> [1] "========================"
#> Analysis of Variance Table
#>
#> Response: y
#> Df Sum Sq Mean Sq F value Pr(>F)
#> trat 17 17.1811 1.01065 2.8833 0.004188 **
#> bloco 2 0.2468 0.12340 0.3521 0.705774
#> Residuals 34 11.9176 0.35052
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#> # A tibble: 2 × 5
#> designacao bloco mfpa_kg_pl rs yp
#> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 10 2 2.75 3.96 1.20
#> 2 14 1 2.87 3.33 1.50
#>
#> NULL
#> [1] "========================"
#> [1] "mspa_1000_g"
#> [1] "========================"
#> Analysis of Variance Table
#>
#> Response: y
#> Df Sum Sq Mean Sq F value Pr(>F)
#> trat 17 0.0189000 0.00111176 4.7764 5.28e-05 ***
#> bloco 2 0.0034194 0.00170972 7.3454 0.002231 **
#> Residuals 34 0.0079139 0.00023276
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#> # A tibble: 2 × 5
#> designacao bloco mspa_1000_g rs yp
#> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 16 1 0.16 3.11 0.126
#> 2 16 3 0.06 -5.23 0.108
#>
#> NULL
#> [1] "========================"
#> [1] "tmspa_percent"
#> [1] "========================"
#> Analysis of Variance Table
#>
#> Response: y
#> Df Sum Sq Mean Sq F value Pr(>F)
#> trat 17 189.000 11.1176 4.7764 5.28e-05 ***
#> bloco 2 34.194 17.0972 7.3454 0.002231 **
#> Residuals 34 79.139 2.3276
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#> # A tibble: 2 × 5
#> designacao bloco tmspa_percent rs yp
#> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 16 1 16 3.11 12.6
#> 2 16 3 6 -5.23 10.7
#>
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#> NULLAnálise exploratória - Multiespectral
Carregando pacotes e banco de dados
data_set <- read_rds("../data/batata-doce-multiespectral-i.rds") |>
drop_na()lista_variaveis <- data_set |> select(r:vari) |> names()
epocas <- data_set |> pull(epoca) |> unique()
# map(lista_variaveis, ~{
# data_set |>
# ggplot(aes(x=!!sym(.x), y = ..density..)) +
# geom_histogram(color="black",fill="gray", bins = 15) +
# labs(title = .x) +
# theme_bw()
# })Análise de resÃduos - Pré-supostos da ANOVA
for(i in seq_along(lista_variaveis)){
for(j in seq_along(epocas)){
print("========================")
print(paste(lista_variaveis[i]," Época: ",epocas[j]))
print("========================")
y <- data_set |> filter(epoca == epocas[j]) |> pull(lista_variaveis[i])
trat <- data_set |> filter(epoca ==epocas[j])|> pull(designacao) |> as_factor()
bloco <- data_set |> filter(epoca == epocas[j]) |> pull(rep) |> as_factor()
mod <- aov(y ~ trat + bloco)
print(anova(mod))
rs <- rstudent(mod)
yp <- predict(mod)
sw_test <- shapiro.test(rs)
sw_test <- round(sw_test$p.value,5)
print(
as_tibble(rs) |>
ggplot(aes(rs)) +
geom_histogram(bins=14,color="black",fill="aquamarine4") +
labs(title = paste(lista_variaveis[i]," Época: ",epocas[j]),
subtitle = paste("Shapiro-Wilk - p-valor: ",sw_test)) +
theme_bw()
)
df_aux <- data_set |>
filter(epoca == epocas[j]) |>
select(designacao, rep,lista_variaveis[i]) |>
add_column(rs,yp) |>
filter(rs > 3 | rs < -3)
# arrange(rs)
if(nrow(df_aux) != 0) print(df_aux)
levene_teste <- lawstat::levene.test(y,trat)
levene_teste <- round(levene_teste$p.value,5)
box_plot <- data_set |>
filter(epoca == epocas[j]) |>
group_by(designacao) |>
mutate(
y_mean = median(!!sym(lista_variaveis[i]),na.rm=TRUE),
designacao = as_factor(designacao)) |>
ungroup() |>
mutate(designacao = designacao |> fct_reorder(y_mean)) |>
ggplot(aes(x=as_factor(designacao),y=!!sym(lista_variaveis[i]),
fill=as_factor(designacao))) +
geom_boxplot() +
scale_fill_viridis_d(option = "magma") +
theme_bw()+
labs(x="Designacao",
title = paste("Levene test - p-valor: ",levene_teste))
print(
box_plot
)
print(cat("\n"))
}
}
#> [1] "========================"
#> [1] "r Época: 1"
#> [1] "========================"
#> Analysis of Variance Table
#>
#> Response: y
#> Df Sum Sq Mean Sq F value Pr(>F)
#> trat 17 5644685 332040 1.3616 0.21972
#> bloco 2 2133339 1066670 4.3740 0.02093 *
#> Residuals 32 7803711 243866
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#>
#> NULL
#> [1] "========================"
#> [1] "r Época: 2"
#> [1] "========================"
#> Analysis of Variance Table
#>
#> Response: y
#> Df Sum Sq Mean Sq F value Pr(>F)
#> trat 17 1439578 84681 0.9109 0.5682
#> bloco 2 315163 157581 1.6951 0.1987
#> Residuals 34 3160685 92961
#>
#> NULL
#> [1] "========================"
#> [1] "r Época: 4"
#> [1] "========================"
#> Analysis of Variance Table
#>
#> Response: y
#> Df Sum Sq Mean Sq F value Pr(>F)
#> trat 17 1364421 80260 0.8461 0.6342
#> bloco 2 237444 118722 1.2515 0.2989
#> Residuals 34 3225369 94864
#>
#> NULL
#> [1] "========================"
#> [1] "r Época: 5"
#> [1] "========================"
#> Analysis of Variance Table
#>
#> Response: y
#> Df Sum Sq Mean Sq F value Pr(>F)
#> trat 17 2429885 142934 1.299 0.250936
#> bloco 2 1615715 807857 7.342 0.002237 **
#> Residuals 34 3741109 110033
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#>
#> NULL
#> [1] "========================"
#> [1] "r Época: 6"
#> [1] "========================"
#> Analysis of Variance Table
#>
#> Response: y
#> Df Sum Sq Mean Sq F value Pr(>F)
#> trat 17 2097094 123358 0.9963 0.484794
#> bloco 2 1977210 988605 7.9841 0.001437 **
#> Residuals 34 4209942 123822
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#>
#> NULL
#> [1] "========================"
#> [1] "r Época: 7"
#> [1] "========================"
#> Analysis of Variance Table
#>
#> Response: y
#> Df Sum Sq Mean Sq F value Pr(>F)
#> trat 17 23256775 1368046 0.9206 0.5584501
#> bloco 2 28795655 14397827 9.6888 0.0004678 ***
#> Residuals 34 50525171 1486034
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#>
#> NULL
#> [1] "========================"
#> [1] "g Época: 1"
#> [1] "========================"
#> Analysis of Variance Table
#>
#> Response: y
#> Df Sum Sq Mean Sq F value Pr(>F)
#> trat 17 4031480 237146 1.4174 0.1923
#> bloco 2 14932896 7466448 44.6270 5.537e-10 ***
#> Residuals 32 5353849 167308
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#>
#> NULL
#> [1] "========================"
#> [1] "g Época: 2"
#> [1] "========================"
#> Analysis of Variance Table
#>
#> Response: y
#> Df Sum Sq Mean Sq F value Pr(>F)
#> trat 17 5507135 323949 2.7326 0.006153 **
#> bloco 2 949713 474856 4.0055 0.027414 *
#> Residuals 34 4030759 118552
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#> # A tibble: 1 × 5
#> designacao rep g rs yp
#> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 7 2 20041. -3.19 20814.
#>
#> NULL
#> [1] "========================"
#> [1] "g Época: 4"
#> [1] "========================"
#> Analysis of Variance Table
#>
#> Response: y
#> Df Sum Sq Mean Sq F value Pr(>F)
#> trat 17 5074978 298528 3.5848 0.0007487 ***
#> bloco 2 1378659 689330 8.2776 0.0011779 **
#> Residuals 34 2831392 83276
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#>
#> NULL
#> [1] "========================"
#> [1] "g Época: 5"
#> [1] "========================"
#> Analysis of Variance Table
#>
#> Response: y
#> Df Sum Sq Mean Sq F value Pr(>F)
#> trat 17 6908059 406356 3.7134 0.0005532 ***
#> bloco 2 882397 441198 4.0318 0.0268375 *
#> Residuals 34 3720632 109430
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#>
#> NULL
#> [1] "========================"
#> [1] "g Época: 6"
#> [1] "========================"
#> Analysis of Variance Table
#>
#> Response: y
#> Df Sum Sq Mean Sq F value Pr(>F)
#> trat 17 4649496 273500 0.9023 0.57681
#> bloco 2 1935305 967652 3.1925 0.05363 .
#> Residuals 34 10305446 303101
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#>
#> NULL
#> [1] "========================"
#> [1] "g Época: 7"
#> [1] "========================"
#> Analysis of Variance Table
#>
#> Response: y
#> Df Sum Sq Mean Sq F value Pr(>F)
#> trat 17 15691067 923004 0.9012 0.578
#> bloco 2 107290445 53645223 52.3770 4.14e-11 ***
#> Residuals 34 34823285 1024214
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#>
#> NULL
#> [1] "========================"
#> [1] "b Época: 1"
#> [1] "========================"
#> Analysis of Variance Table
#>
#> Response: y
#> Df Sum Sq Mean Sq F value Pr(>F)
#> trat 17 1806.96 106.29 2.2799 0.021638 *
#> bloco 2 686.75 343.38 7.3653 0.002338 **
#> Residuals 32 1491.86 46.62
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#> # A tibble: 1 × 5
#> designacao rep b rs yp
#> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 11 1 179. 3.03 164.
#>
#> NULL
#> [1] "========================"
#> [1] "b Época: 2"
#> [1] "========================"
#> Analysis of Variance Table
#>
#> Response: y
#> Df Sum Sq Mean Sq F value Pr(>F)
#> trat 17 1635.55 96.21 4.2208 0.0001743 ***
#> bloco 2 931.43 465.71 20.4313 1.488e-06 ***
#> Residuals 34 775.00 22.79
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#>
#> NULL
#> [1] "========================"
#> [1] "b Época: 4"
#> [1] "========================"
#> Analysis of Variance Table
#>
#> Response: y
#> Df Sum Sq Mean Sq F value Pr(>F)
#> trat 17 4450.8 261.81 0.9721 0.50778
#> bloco 2 1792.2 896.11 3.3274 0.04789 *
#> Residuals 34 9156.7 269.32
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#> # A tibble: 2 × 5
#> designacao rep b rs yp
#> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 1 1 255 20.0 182.
#> 2 1 3 133. -3.30 171.
#>
#> NULL
#> [1] "========================"
#> [1] "b Época: 5"
#> [1] "========================"
#> Analysis of Variance Table
#>
#> Response: y
#> Df Sum Sq Mean Sq F value Pr(>F)
#> trat 17 1743.39 102.55 3.6111 0.0007036 ***
#> bloco 2 1007.60 503.80 17.7397 5.292e-06 ***
#> Residuals 34 965.58 28.40
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#>
#> NULL
#> [1] "========================"
#> [1] "b Época: 6"
#> [1] "========================"
#> Analysis of Variance Table
#>
#> Response: y
#> Df Sum Sq Mean Sq F value Pr(>F)
#> trat 17 1740.2 102.36 3.2805 0.001557 **
#> bloco 2 1279.8 639.91 20.5073 1.438e-06 ***
#> Residuals 34 1060.9 31.20
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#>
#> NULL
#> [1] "========================"
#> [1] "b Época: 7"
#> [1] "========================"
#> Analysis of Variance Table
#>
#> Response: y
#> Df Sum Sq Mean Sq F value Pr(>F)
#> trat 17 500.22 29.425 1.2428 0.2861
#> bloco 2 105.47 52.736 2.2274 0.1233
#> Residuals 34 804.96 23.675
#>
#> NULL
#> [1] "========================"
#> [1] "nir Época: 1"
#> [1] "========================"
#> Analysis of Variance Table
#>
#> Response: y
#> Df Sum Sq Mean Sq F value Pr(>F)
#> trat 17 96100339 5652961 3.1006 0.002813 **
#> bloco 2 166422341 83211171 45.6408 4.247e-10 ***
#> Residuals 32 58341655 1823177
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#>
#> NULL
#> [1] "========================"
#> [1] "nir Época: 2"
#> [1] "========================"
#> Analysis of Variance Table
#>
#> Response: y
#> Df Sum Sq Mean Sq F value Pr(>F)
#> trat 17 163621353 9624785 4.5347 8.800e-05 ***
#> bloco 2 89810311 44905156 21.1569 1.074e-06 ***
#> Residuals 34 72164324 2122480
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#>
#> NULL
#> [1] "========================"
#> [1] "nir Época: 4"
#> [1] "========================"
#> Analysis of Variance Table
#>
#> Response: y
#> Df Sum Sq Mean Sq F value Pr(>F)
#> trat 17 185793828 10929049 6.4649 2.087e-06 ***
#> bloco 2 163848396 81924198 48.4609 1.112e-10 ***
#> Residuals 34 57477764 1690522
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#>
#> NULL
#> [1] "========================"
#> [1] "nir Época: 5"
#> [1] "========================"
#> Analysis of Variance Table
#>
#> Response: y
#> Df Sum Sq Mean Sq F value Pr(>F)
#> trat 17 228925047 13466179 5.0962 2.74e-05 ***
#> bloco 2 146168768 73084384 27.6586 7.40e-08 ***
#> Residuals 34 89840767 2642376
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#>
#> NULL
#> [1] "========================"
#> [1] "nir Época: 6"
#> [1] "========================"
#> Analysis of Variance Table
#>
#> Response: y
#> Df Sum Sq Mean Sq F value Pr(>F)
#> trat 17 814049890 47885288 4.8437 4.591e-05 ***
#> bloco 2 138943217 69471608 7.0272 0.00279 **
#> Residuals 34 336125270 9886037
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#> # A tibble: 1 × 5
#> designacao rep nir rs yp
#> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 14 3 22574. -3.17 29600.
#>
#> NULL
#> [1] "========================"
#> [1] "nir Época: 7"
#> [1] "========================"
#> Analysis of Variance Table
#>
#> Response: y
#> Df Sum Sq Mean Sq F value Pr(>F)
#> trat 17 1374181861 80834227 1.0736 0.414928
#> bloco 2 1351730821 675865411 8.9764 0.000741 ***
#> Residuals 34 2559986050 75293707
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#> # A tibble: 1 × 5
#> designacao rep nir rs yp
#> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 9 2 20823. -3.29 40759.
#>
#> NULL
#> [1] "========================"
#> [1] "red_edge Época: 1"
#> [1] "========================"
#> Analysis of Variance Table
#>
#> Response: y
#> Df Sum Sq Mean Sq F value Pr(>F)
#> trat 17 5128129 301655 1.3819 0.2094
#> bloco 2 22011269 11005634 50.4185 1.286e-10 ***
#> Residuals 32 6985141 218286
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#>
#> NULL
#> [1] "========================"
#> [1] "red_edge Época: 2"
#> [1] "========================"
#> Analysis of Variance Table
#>
#> Response: y
#> Df Sum Sq Mean Sq F value Pr(>F)
#> trat 17 6932745 407809 0.7202 0.7615
#> bloco 2 23454808 11727404 20.7103 1.312e-06 ***
#> Residuals 34 19252830 566260
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#>
#> NULL
#> [1] "========================"
#> [1] "red_edge Época: 4"
#> [1] "========================"
#> Analysis of Variance Table
#>
#> Response: y
#> Df Sum Sq Mean Sq F value Pr(>F)
#> trat 17 11736032 690355 0.8209 0.6601
#> bloco 2 48222823 24111412 28.6716 5.054e-08 ***
#> Residuals 34 28592381 840952
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#> # A tibble: 1 × 5
#> designacao rep red_edge rs yp
#> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 9 2 23300. -3.31 25418.
#>
#> NULL
#> [1] "========================"
#> [1] "red_edge Época: 5"
#> [1] "========================"
#> Analysis of Variance Table
#>
#> Response: y
#> Df Sum Sq Mean Sq F value Pr(>F)
#> trat 17 3887897 228700 0.3232 0.9921
#> bloco 2 36270352 18135176 25.6255 1.634e-07 ***
#> Residuals 34 24061775 707699
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#>
#> NULL
#> [1] "========================"
#> [1] "red_edge Época: 6"
#> [1] "========================"
#> Analysis of Variance Table
#>
#> Response: y
#> Df Sum Sq Mean Sq F value Pr(>F)
#> trat 17 24549652 1444097 2.5702 0.009357 **
#> bloco 2 160975995 80487998 143.2550 < 2.2e-16 ***
#> Residuals 34 19102938 561851
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#>
#> NULL
#> [1] "========================"
#> [1] "red_edge Época: 7"
#> [1] "========================"
#> Analysis of Variance Table
#>
#> Response: y
#> Df Sum Sq Mean Sq F value Pr(>F)
#> trat 17 88487496 5205147 1.2265 0.297
#> bloco 2 330602479 165301240 38.9496 1.604e-09 ***
#> Residuals 34 144295100 4243974
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#>
#> NULL
#> [1] "========================"
#> [1] "ndvi Época: 1"
#> [1] "========================"
#> Analysis of Variance Table
#>
#> Response: y
#> Df Sum Sq Mean Sq F value Pr(>F)
#> trat 17 0.056511 0.0033242 2.1743 0.02836 *
#> bloco 2 0.059519 0.0297597 19.4650 2.945e-06 ***
#> Residuals 32 0.048924 0.0015289
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#> # A tibble: 2 × 5
#> designacao rep ndvi rs yp
#> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 16 1 -0.0764 -3.44 0.0154
#> 2 16 3 0.0873 5.85 -0.0397
#>
#> NULL
#> [1] "========================"
#> [1] "ndvi Época: 2"
#> [1] "========================"
#> Analysis of Variance Table
#>
#> Response: y
#> Df Sum Sq Mean Sq F value Pr(>F)
#> trat 17 0.077870 0.0045806 4.8579 4.458e-05 ***
#> bloco 2 0.054021 0.0270107 28.6457 5.103e-08 ***
#> Residuals 34 0.032059 0.0009429
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#>
#> NULL
#> [1] "========================"
#> [1] "ndvi Época: 4"
#> [1] "========================"
#> Analysis of Variance Table
#>
#> Response: y
#> Df Sum Sq Mean Sq F value Pr(>F)
#> trat 17 0.060480 0.0035577 4.9381 3.780e-05 ***
#> bloco 2 0.061047 0.0305236 42.3670 5.853e-10 ***
#> Residuals 34 0.024496 0.0007205
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#>
#> NULL
#> [1] "========================"
#> [1] "ndvi Época: 5"
#> [1] "========================"
#> Analysis of Variance Table
#>
#> Response: y
#> Df Sum Sq Mean Sq F value Pr(>F)
#> trat 17 0.088152 0.005185 4.5149 9.182e-05 ***
#> bloco 2 0.064243 0.032121 27.9676 6.582e-08 ***
#> Residuals 34 0.039050 0.001149
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#>
#> NULL
#> [1] "========================"
#> [1] "ndvi Época: 6"
#> [1] "========================"
#> Analysis of Variance Table
#>
#> Response: y
#> Df Sum Sq Mean Sq F value Pr(>F)
#> trat 17 0.199961 0.011762 4.5566 8.397e-05 ***
#> bloco 2 0.073812 0.036906 14.2970 3.120e-05 ***
#> Residuals 34 0.087767 0.002581
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#> # A tibble: 1 × 5
#> designacao rep ndvi rs yp
#> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 14 3 0.0328 -3.20 0.147
#>
#> NULL
#> [1] "========================"
#> [1] "ndvi Época: 7"
#> [1] "========================"
#> Analysis of Variance Table
#>
#> Response: y
#> Df Sum Sq Mean Sq F value Pr(>F)
#> trat 17 0.122200 0.007188 0.8513 0.62890
#> bloco 2 0.068734 0.034367 4.0698 0.02603 *
#> Residuals 34 0.287106 0.008444
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#> # A tibble: 1 × 5
#> designacao rep ndvi rs yp
#> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 9 2 0.0938 -3.12 0.297
#>
#> NULL
#> [1] "========================"
#> [1] "gndvi Época: 1"
#> [1] "========================"
#> Analysis of Variance Table
#>
#> Response: y
#> Df Sum Sq Mean Sq F value Pr(>F)
#> trat 17 0.040335 0.0023727 3.1045 0.002787 **
#> bloco 2 0.035372 0.0176861 23.1415 6.077e-07 ***
#> Residuals 32 0.024456 0.0007643
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#>
#> NULL
#> [1] "========================"
#> [1] "gndvi Época: 2"
#> [1] "========================"
#> Analysis of Variance Table
#>
#> Response: y
#> Df Sum Sq Mean Sq F value Pr(>F)
#> trat 17 0.062517 0.0036775 4.2166 0.0001759 ***
#> bloco 2 0.029763 0.0148814 17.0630 7.393e-06 ***
#> Residuals 34 0.029653 0.0008721
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#> # A tibble: 1 × 5
#> designacao rep gndvi rs yp
#> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 7 2 0.145 3.12 0.0802
#>
#> NULL
#> [1] "========================"
#> [1] "gndvi Época: 4"
#> [1] "========================"
#> Analysis of Variance Table
#>
#> Response: y
#> Df Sum Sq Mean Sq F value Pr(>F)
#> trat 17 0.061623 0.0036249 5.1945 2.250e-05 ***
#> bloco 2 0.062059 0.0310297 44.4658 3.242e-10 ***
#> Residuals 34 0.023726 0.0006978
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#>
#> NULL
#> [1] "========================"
#> [1] "gndvi Época: 5"
#> [1] "========================"
#> Analysis of Variance Table
#>
#> Response: y
#> Df Sum Sq Mean Sq F value Pr(>F)
#> trat 17 0.090934 0.0053490 4.5122 9.235e-05 ***
#> bloco 2 0.050324 0.0251618 21.2254 1.041e-06 ***
#> Residuals 34 0.040306 0.0011855
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#>
#> NULL
#> [1] "========================"
#> [1] "gndvi Época: 6"
#> [1] "========================"
#> Analysis of Variance Table
#>
#> Response: y
#> Df Sum Sq Mean Sq F value Pr(>F)
#> trat 17 0.183034 0.0107667 3.9898 0.0002926 ***
#> bloco 2 0.046750 0.0233750 8.6620 0.0009113 ***
#> Residuals 34 0.091751 0.0026986
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#> # A tibble: 1 × 5
#> designacao rep gndvi rs yp
#> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 14 3 0.0324 -3.23 0.150
#>
#> NULL
#> [1] "========================"
#> [1] "gndvi Época: 7"
#> [1] "========================"
#> Analysis of Variance Table
#>
#> Response: y
#> Df Sum Sq Mean Sq F value Pr(>F)
#> trat 17 0.19450 0.011441 1.0122 0.46985
#> bloco 2 0.09261 0.046304 4.0967 0.02547 *
#> Residuals 34 0.38429 0.011303
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#> # A tibble: 1 × 5
#> designacao rep gndvi rs yp
#> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 9 2 0.0158 -3.05 0.247
#>
#> NULL
#> [1] "========================"
#> [1] "savi Época: 1"
#> [1] "========================"
#> Analysis of Variance Table
#>
#> Response: y
#> Df Sum Sq Mean Sq F value Pr(>F)
#> trat 17 0.125309 0.007371 4.1087 0.0002837 ***
#> bloco 2 0.159384 0.079692 44.4206 5.847e-10 ***
#> Residuals 32 0.057409 0.001794
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#>
#> NULL
#> [1] "========================"
#> [1] "savi Época: 2"
#> [1] "========================"
#> Analysis of Variance Table
#>
#> Response: y
#> Df Sum Sq Mean Sq F value Pr(>F)
#> trat 17 0.174173 0.010245 4.8002 5.025e-05 ***
#> bloco 2 0.125200 0.062600 29.3294 3.963e-08 ***
#> Residuals 34 0.072569 0.002134
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#>
#> NULL
#> [1] "========================"
#> [1] "savi Época: 4"
#> [1] "========================"
#> Analysis of Variance Table
#>
#> Response: y
#> Df Sum Sq Mean Sq F value Pr(>F)
#> trat 17 0.137322 0.008078 4.9156 3.958e-05 ***
#> bloco 2 0.138688 0.069344 42.1983 6.143e-10 ***
#> Residuals 34 0.055872 0.001643
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#>
#> NULL
#> [1] "========================"
#> [1] "savi Época: 5"
#> [1] "========================"
#> Analysis of Variance Table
#>
#> Response: y
#> Df Sum Sq Mean Sq F value Pr(>F)
#> trat 17 0.19834 0.011667 4.5149 9.182e-05 ***
#> bloco 2 0.14454 0.072271 27.9676 6.582e-08 ***
#> Residuals 34 0.08786 0.002584
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#>
#> NULL
#> [1] "========================"
#> [1] "savi Época: 6"
#> [1] "========================"
#> Analysis of Variance Table
#>
#> Response: y
#> Df Sum Sq Mean Sq F value Pr(>F)
#> trat 17 0.44990 0.026465 4.5566 8.397e-05 ***
#> bloco 2 0.16607 0.083037 14.2969 3.120e-05 ***
#> Residuals 34 0.19747 0.005808
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#> # A tibble: 1 × 5
#> designacao rep savi rs yp
#> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 14 3 0.0492 -3.20 0.221
#>
#> NULL
#> [1] "========================"
#> [1] "savi Época: 7"
#> [1] "========================"
#> Analysis of Variance Table
#>
#> Response: y
#> Df Sum Sq Mean Sq F value Pr(>F)
#> trat 17 0.27495 0.016173 0.8513 0.62890
#> bloco 2 0.15465 0.077325 4.0699 0.02602 *
#> Residuals 34 0.64598 0.018999
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#> # A tibble: 1 × 5
#> designacao rep savi rs yp
#> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 9 2 0.141 -3.12 0.445
#>
#> NULL
#> [1] "========================"
#> [1] "osavi Época: 1"
#> [1] "========================"
#> Analysis of Variance Table
#>
#> Response: y
#> Df Sum Sq Mean Sq F value Pr(>F)
#> trat 17 0.074564 0.004386 4.0407 0.0003286 ***
#> bloco 2 0.093156 0.046578 42.9096 8.769e-10 ***
#> Residuals 32 0.034736 0.001085
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#>
#> NULL
#> [1] "========================"
#> [1] "osavi Época: 2"
#> [1] "========================"
#> Analysis of Variance Table
#>
#> Response: y
#> Df Sum Sq Mean Sq F value Pr(>F)
#> trat 17 0.104781 0.006164 4.8579 4.458e-05 ***
#> bloco 2 0.072690 0.036345 28.6457 5.103e-08 ***
#> Residuals 34 0.043139 0.001269
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#>
#> NULL
#> [1] "========================"
#> [1] "osavi Época: 4"
#> [1] "========================"
#> Analysis of Variance Table
#>
#> Response: y
#> Df Sum Sq Mean Sq F value Pr(>F)
#> trat 17 0.081381 0.004787 4.9381 3.780e-05 ***
#> bloco 2 0.082144 0.041072 42.3670 5.853e-10 ***
#> Residuals 34 0.032961 0.000969
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#>
#> NULL
#> [1] "========================"
#> [1] "osavi Época: 5"
#> [1] "========================"
#> Analysis of Variance Table
#>
#> Response: y
#> Df Sum Sq Mean Sq F value Pr(>F)
#> trat 17 0.122858 0.007227 4.3643 0.0001272 ***
#> bloco 2 0.089630 0.044815 27.0633 9.296e-08 ***
#> Residuals 34 0.056302 0.001656
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#>
#> NULL
#> [1] "========================"
#> [1] "osavi Época: 6"
#> [1] "========================"
#> Analysis of Variance Table
#>
#> Response: y
#> Df Sum Sq Mean Sq F value Pr(>F)
#> trat 17 0.269066 0.015827 4.5566 8.397e-05 ***
#> bloco 2 0.099321 0.049660 14.2969 3.120e-05 ***
#> Residuals 34 0.118099 0.003473
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#> # A tibble: 1 × 5
#> designacao rep osavi rs yp
#> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 14 3 0.0380 -3.20 0.171
#>
#> NULL
#> [1] "========================"
#> [1] "osavi Época: 7"
#> [1] "========================"
#> Analysis of Variance Table
#>
#> Response: y
#> Df Sum Sq Mean Sq F value Pr(>F)
#> trat 17 0.15415 0.009068 0.7427 0.73936
#> bloco 2 0.07608 0.038039 3.1157 0.05722 .
#> Residuals 34 0.41510 0.012209
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#>
#> NULL
#> [1] "========================"
#> [1] "ndre Época: 1"
#> [1] "========================"
#> Analysis of Variance Table
#>
#> Response: y
#> Df Sum Sq Mean Sq F value Pr(>F)
#> trat 17 0.032583 0.0019166 3.0572 0.003123 **
#> bloco 2 0.028568 0.0142839 22.7838 7.039e-07 ***
#> Residuals 32 0.020062 0.0006269
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#>
#> NULL
#> [1] "========================"
#> [1] "ndre Época: 2"
#> [1] "========================"
#> Analysis of Variance Table
#>
#> Response: y
#> Df Sum Sq Mean Sq F value Pr(>F)
#> trat 17 0.060707 0.0035710 3.8834 0.0003730 ***
#> bloco 2 0.016138 0.0080688 8.7747 0.0008459 ***
#> Residuals 34 0.031265 0.0009195
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#>
#> NULL
#> [1] "========================"
#> [1] "ndre Época: 4"
#> [1] "========================"
#> Analysis of Variance Table
#>
#> Response: y
#> Df Sum Sq Mean Sq F value Pr(>F)
#> trat 17 0.062260 0.0036623 3.7593 0.0004970 ***
#> bloco 2 0.023093 0.0115464 11.8522 0.0001243 ***
#> Residuals 34 0.033123 0.0009742
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#>
#> NULL
#> [1] "========================"
#> [1] "ndre Época: 5"
#> [1] "========================"
#> Analysis of Variance Table
#>
#> Response: y
#> Df Sum Sq Mean Sq F value Pr(>F)
#> trat 17 0.079043 0.0046496 4.0802 0.0002385 ***
#> bloco 2 0.011771 0.0058855 5.1647 0.0109996 *
#> Residuals 34 0.038745 0.0011396
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#>
#> NULL
#> [1] "========================"
#> [1] "ndre Época: 6"
#> [1] "========================"
#> Analysis of Variance Table
#>
#> Response: y
#> Df Sum Sq Mean Sq F value Pr(>F)
#> trat 17 0.244314 0.0143714 4.6363 7.088e-05 ***
#> bloco 2 0.017213 0.0086066 2.7766 0.07639 .
#> Residuals 34 0.105391 0.0030997
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#>
#> NULL
#> [1] "========================"
#> [1] "ndre Época: 7"
#> [1] "========================"
#> Analysis of Variance Table
#>
#> Response: y
#> Df Sum Sq Mean Sq F value Pr(>F)
#> trat 17 0.17140 0.010082 0.9849 0.4956
#> bloco 2 0.31750 0.158752 15.5072 1.637e-05 ***
#> Residuals 34 0.34807 0.010237
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#> # A tibble: 1 × 5
#> designacao rep ndre rs yp
#> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 9 2 -0.137 -3.47 0.105
#>
#> NULL
#> [1] "========================"
#> [1] "vari Época: 1"
#> [1] "========================"
#> Analysis of Variance Table
#>
#> Response: y
#> Df Sum Sq Mean Sq F value Pr(>F)
#> trat 17 0.0046704 0.0002747 2.0921 0.03501 *
#> bloco 2 0.0064776 0.0032388 24.6644 3.3e-07 ***
#> Residuals 32 0.0042021 0.0001313
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#>
#> NULL
#> [1] "========================"
#> [1] "vari Época: 2"
#> [1] "========================"
#> Analysis of Variance Table
#>
#> Response: y
#> Df Sum Sq Mean Sq F value Pr(>F)
#> trat 17 0.0043419 2.554e-04 2.7398 0.006041 **
#> bloco 2 0.0034920 1.746e-03 18.7299 3.282e-06 ***
#> Residuals 34 0.0031695 9.322e-05
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#>
#> NULL
#> [1] "========================"
#> [1] "vari Época: 4"
#> [1] "========================"
#> Analysis of Variance Table
#>
#> Response: y
#> Df Sum Sq Mean Sq F value Pr(>F)
#> trat 17 0.00252591 1.4858e-04 1.9108 0.05305 .
#> bloco 2 0.00012706 6.3531e-05 0.8170 0.45023
#> Residuals 34 0.00264386 7.7761e-05
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#>
#> NULL
#> [1] "========================"
#> [1] "vari Época: 5"
#> [1] "========================"
#> Analysis of Variance Table
#>
#> Response: y
#> Df Sum Sq Mean Sq F value Pr(>F)
#> trat 17 0.0027279 0.00016046 1.3645 0.214575
#> bloco 2 0.0017842 0.00089211 7.5861 0.001888 **
#> Residuals 34 0.0039983 0.00011760
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#> # A tibble: 1 × 5
#> designacao rep vari rs yp
#> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 4 3 0.0202 3.27 -0.00459
#>
#> NULL
#> [1] "========================"
#> [1] "vari Época: 6"
#> [1] "========================"
#> Analysis of Variance Table
#>
#> Response: y
#> Df Sum Sq Mean Sq F value Pr(>F)
#> trat 17 0.047623 0.0028014 1.1834 0.3274
#> bloco 2 0.008328 0.0041642 1.7591 0.1875
#> Residuals 34 0.080487 0.0023673
#> # A tibble: 3 × 5
#> designacao rep vari rs yp
#> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 3 2 -0.177 -3.20 -0.0677
#> 2 12 1 -0.0399 -3.65 0.0809
#> 3 12 3 0.293 7.84 0.111
#>
#> NULL
#> [1] "========================"
#> [1] "vari Época: 7"
#> [1] "========================"
#> Analysis of Variance Table
#>
#> Response: y
#> Df Sum Sq Mean Sq F value Pr(>F)
#> trat 17 0.017385 0.0010226 1.0832 0.4067
#> bloco 2 0.034355 0.0171774 18.1946 4.242e-06 ***
#> Residuals 34 0.032099 0.0009441
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#>
#> NULLAnálise exploratória - Batata Doce - VERÃO
Carregando pacotes e banco de dados
data_set <- read_rds("../data/batata-doce-ednaldo-v.rds")lista_variaveis <- data_set |> select(pt:tmspa_percent) |> names()
# map(lista_variaveis, ~{
# data_set |>
# ggplot(aes(x=!!sym(.x), y = ..density..)) +
# geom_histogram(color="black",fill="gray", bins = 15) +
# labs(title = .x) +
# theme_bw()
# })Análise de resÃduos - Pré-supostos da ANOVA
map(lista_variaveis, ~{
print("========================")
print(.x)
print("========================")
y <- data_set |> pull(!!sym(.x))
trat <- data_set |> pull(designacao) |> as_factor()
bloco <- data_set |> pull(bloco) |> as_factor()
mod <- aov(y ~ trat + bloco)
print(anova(mod))
rs <- rstudent(mod)
yp <- predict(mod)
sw_test <- shapiro.test(rs)
sw_test <- round(sw_test$p.value,5)
print(
as_tibble(rs) |>
ggplot(aes(rs)) +
geom_histogram(bins=14,color="black",fill="aquamarine4") +
labs(title = .x,
subtitle = paste("Shapiro-Wilk - p-valor: ",sw_test)) +
theme_bw()
)
df_aux <- data_set |>
select(designacao, bloco,!!sym(.x)) |>
add_column(rs,yp) |>
filter(rs > 3 | rs < -3)
# arrange(rs)
if(nrow(df_aux) != 0) print(df_aux)
levene_teste <- lawstat::levene.test(y,trat)
levene_teste <- round(levene_teste$p.value,5)
box_plot <- data_set |>
group_by(designacao) |>
mutate(
y_mean = median(!!sym(.x),na.rm=TRUE),
designacao = as_factor(designacao)) |>
ungroup() |>
mutate(designacao = designacao |> fct_reorder(y_mean)) |>
ggplot(aes(x=as_factor(designacao),y=!!sym(.x),
fill=as_factor(designacao))) +
geom_boxplot() +
scale_fill_viridis_d(option = "magma") +
theme_bw()+
labs(x="Designacao",
title = paste("Levene test - p-valor: ",levene_teste))
print(
box_plot
)
print(cat("\n"))
})
#> [1] "========================"
#> [1] "pt"
#> [1] "========================"
#> Analysis of Variance Table
#>
#> Response: y
#> Df Sum Sq Mean Sq F value Pr(>F)
#> trat 17 7.1664 0.42155 5.0518 2.998e-05 ***
#> bloco 2 0.4789 0.23944 2.8694 0.07055 .
#> Residuals 34 2.8372 0.08345
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#>
#> NULL
#> [1] "========================"
#> [1] "pc"
#> [1] "========================"
#> Analysis of Variance Table
#>
#> Response: y
#> Df Sum Sq Mean Sq F value Pr(>F)
#> trat 17 7.1686 0.42168 5.0409 3.064e-05 ***
#> bloco 2 0.4867 0.24336 2.9092 0.06819 .
#> Residuals 34 2.8441 0.08365
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#>
#> NULL
#> [1] "========================"
#> [1] "pnc"
#> [1] "========================"
#> Analysis of Variance Table
#>
#> Response: y
#> Df Sum Sq Mean Sq F value Pr(>F)
#> trat 17 0.0111922 0.00065836 3.8580 0.0003955 ***
#> bloco 2 0.0003301 0.00016503 0.9671 0.3904093
#> Residuals 34 0.0058021 0.00017065
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#>
#> NULL
#> [1] "========================"
#> [1] "nrt"
#> [1] "========================"
#> Analysis of Variance Table
#>
#> Response: y
#> Df Sum Sq Mean Sq F value Pr(>F)
#> trat 17 103.250 6.0735 14.1370 9.351e-11 ***
#> bloco 2 1.774 0.8870 2.0646 0.1425
#> Residuals 34 14.607 0.4296
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#>
#> NULL
#> [1] "========================"
#> [1] "nrc"
#> [1] "========================"
#> Analysis of Variance Table
#>
#> Response: y
#> Df Sum Sq Mean Sq F value Pr(>F)
#> trat 17 74.941 4.4083 14.1773 8.985e-11 ***
#> bloco 2 1.559 0.7794 2.5064 0.09652 .
#> Residuals 34 10.572 0.3109
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#>
#> NULL
#> [1] "========================"
#> [1] "nrnc"
#> [1] "========================"
#> Analysis of Variance Table
#>
#> Response: y
#> Df Sum Sq Mean Sq F value Pr(>F)
#> trat 17 6.7508 0.39711 5.9425 5.349e-06 ***
#> bloco 2 0.0849 0.04246 0.6353 0.5359
#> Residuals 34 2.2721 0.06683
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#> # A tibble: 1 × 5
#> designacao bloco nrnc rs yp
#> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 16 3 0.333 -3.41 0.944
#>
#> NULL
#> [1] "========================"
#> [1] "msr_100_g"
#> [1] "========================"
#> Analysis of Variance Table
#>
#> Response: y
#> Df Sum Sq Mean Sq F value Pr(>F)
#> trat 17 0.00110824 6.5191e-05 9.7436 1.42e-08 ***
#> bloco 2 0.00000215 1.0740e-06 0.1605 0.8524
#> Residuals 34 0.00022748 6.6910e-06
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#>
#> NULL
#> [1] "========================"
#> [1] "mr"
#> [1] "========================"
#> Analysis of Variance Table
#>
#> Response: y
#> Df Sum Sq Mean Sq F value Pr(>F)
#> trat 17 0.45046 0.026497 2.6384 0.007842 **
#> bloco 2 0.06187 0.030936 3.0803 0.058956 .
#> Residuals 34 0.34146 0.010043
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#>
#> NULL
#> [1] "========================"
#> [1] "mrc"
#> [1] "========================"
#> Analysis of Variance Table
#>
#> Response: y
#> Df Sum Sq Mean Sq F value Pr(>F)
#> trat 17 0.50393 0.029643 2.0082 0.04102 *
#> bloco 2 0.07618 0.038088 2.5804 0.09051 .
#> Residuals 34 0.50187 0.014761
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#> # A tibble: 2 × 5
#> designacao bloco mrc rs yp
#> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 6 1 0.296 -3.06 0.560
#> 2 17 1 0.509 3.15 0.239
#>
#> NULL
#> [1] "========================"
#> [1] "tms_percent"
#> [1] "========================"
#> Analysis of Variance Table
#>
#> Response: y
#> Df Sum Sq Mean Sq F value Pr(>F)
#> trat 17 1108.24 65.191 9.7436 1.42e-08 ***
#> bloco 2 2.15 1.074 0.1605 0.8524
#> Residuals 34 227.48 6.691
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#>
#> NULL
#> [1] "========================"
#> [1] "ptms"
#> [1] "========================"
#> Analysis of Variance Table
#>
#> Response: y
#> Df Sum Sq Mean Sq F value Pr(>F)
#> trat 17 0.41544 0.0244378 4.6263 7.24e-05 ***
#> bloco 2 0.02957 0.0147836 2.7987 0.07496 .
#> Residuals 34 0.17960 0.0052824
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#>
#> NULL
#> [1] "========================"
#> [1] "ptms_kg_ha"
#> [1] "========================"
#> Analysis of Variance Table
#>
#> Response: y
#> Df Sum Sq Mean Sq F value Pr(>F)
#> trat 17 16557052 973944 4.6263 7.24e-05 ***
#> bloco 2 1178370 589185 2.7987 0.07496 .
#> Residuals 34 7157793 210523
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#>
#> NULL
#> [1] "========================"
#> [1] "mfpa_kg"
#> [1] "========================"
#> Analysis of Variance Table
#>
#> Response: y
#> Df Sum Sq Mean Sq F value Pr(>F)
#> trat 17 9.6992 0.57054 4.4305 0.0001101 ***
#> bloco 2 0.7243 0.36214 2.8121 0.0740963 .
#> Residuals 34 4.3784 0.12878
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#> # A tibble: 1 × 5
#> designacao bloco mfpa_kg rs yp
#> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 17 2 3.01 3.14 2.21
#>
#> NULL
#> [1] "========================"
#> [1] "mspa_1000_g"
#> [1] "========================"
#> Analysis of Variance Table
#>
#> Response: y
#> Df Sum Sq Mean Sq F value Pr(>F)
#> trat 17 0.0066079 0.00038870 1.4289 0.1833659
#> bloco 2 0.0052343 0.00261713 9.6207 0.0004885 ***
#> Residuals 34 0.0092491 0.00027203
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#>
#> NULL
#> [1] "========================"
#> [1] "tmspa_percent"
#> [1] "========================"
#> Analysis of Variance Table
#>
#> Response: y
#> Df Sum Sq Mean Sq F value Pr(>F)
#> trat 17 66.079 3.8870 1.4289 0.1833659
#> bloco 2 52.343 26.1713 9.6207 0.0004885 ***
#> Residuals 34 92.491 2.7203
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#>
#> NULL
#> [[1]]
#> NULL
#>
#> [[2]]
#> NULL
#>
#> [[3]]
#> NULL
#>
#> [[4]]
#> NULL
#>
#> [[5]]
#> NULL
#>
#> [[6]]
#> NULL
#>
#> [[7]]
#> NULL
#>
#> [[8]]
#> NULL
#>
#> [[9]]
#> NULL
#>
#> [[10]]
#> NULL
#>
#> [[11]]
#> NULL
#>
#> [[12]]
#> NULL
#>
#> [[13]]
#> NULL
#>
#> [[14]]
#> NULL
#>
#> [[15]]
#> NULLAnálise exploratória - Multiespectral
Carregando pacotes e banco de dados
data_set <- read_rds("../data/batata-doce-multiespectral-v.rds")lista_variaveis <- data_set |> select(r:vari) |> names()
epocas <- data_set |> pull(epoca) |> unique()
# map(lista_variaveis, ~{
# data_set |>
# ggplot(aes(x=!!sym(.x), y = ..density..)) +
# geom_histogram(color="black",fill="gray", bins = 15) +
# labs(title = .x) +
# theme_bw()
# })Análise de resÃduos - Pré-supostos da ANOVA
for(i in seq_along(lista_variaveis)){
for(j in seq_along(epocas)){
print("========================")
print(paste(lista_variaveis[i]," Época: ",epocas[j]))
print("========================")
y <- data_set |> filter(epoca == epocas[j]) |> pull(lista_variaveis[i])
trat <- data_set |> filter(epoca ==epocas[j])|> pull(designacao) |> as_factor()
bloco <- data_set |> filter(epoca == epocas[j]) |> pull(rep) |> as_factor()
mod <- aov(y ~ trat + bloco)
print(anova(mod))
rs <- rstudent(mod)
yp <- predict(mod)
sw_test <- shapiro.test(rs)
sw_test <- round(sw_test$p.value,5)
print(
as_tibble(rs) |>
ggplot(aes(rs)) +
geom_histogram(bins=14,color="black",fill="aquamarine4") +
labs(title = paste(lista_variaveis[i]," Época: ",epocas[j]),
subtitle = paste("Shapiro-Wilk - p-valor: ",sw_test)) +
theme_bw()
)
df_aux <- data_set |>
filter(epoca == epocas[j]) |>
select(designacao, rep,lista_variaveis[i]) |>
add_column(rs,yp) |>
filter(rs > 3 | rs < -3)
# arrange(rs)
if(nrow(df_aux) != 0) print(df_aux)
levene_teste <- lawstat::levene.test(y,trat)
levene_teste <- round(levene_teste$p.value,5)
box_plot <- data_set |>
filter(epoca == epocas[j]) |>
group_by(designacao) |>
mutate(
y_mean = median(!!sym(lista_variaveis[i]),na.rm=TRUE),
designacao = as_factor(designacao)) |>
ungroup() |>
mutate(designacao = designacao |> fct_reorder(y_mean)) |>
ggplot(aes(x=as_factor(designacao),y=!!sym(lista_variaveis[i]),
fill=as_factor(designacao))) +
geom_boxplot() +
scale_fill_viridis_d(option = "magma") +
theme_bw()+
labs(x="Designacao",
title = paste("Levene test - p-valor: ",levene_teste))
print(
box_plot
)
print(cat("\n"))
}
}
#> [1] "========================"
#> [1] "r Época: 1"
#> [1] "========================"
#> Analysis of Variance Table
#>
#> Response: y
#> Df Sum Sq Mean Sq F value Pr(>F)
#> trat 17 2057358 121021 0.9087 0.57039
#> bloco 2 1509206 754603 5.6660 0.00752 **
#> Residuals 34 4528110 133180
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#>
#> NULL
#> [1] "========================"
#> [1] "r Época: 3"
#> [1] "========================"
#> Analysis of Variance Table
#>
#> Response: y
#> Df Sum Sq Mean Sq F value Pr(>F)
#> trat 17 25991312 1528901 0.9059 0.5732
#> bloco 2 424308 212154 0.1257 0.8823
#> Residuals 34 57383904 1687762
#>
#> NULL
#> [1] "========================"
#> [1] "r Época: 4"
#> [1] "========================"
#> Analysis of Variance Table
#>
#> Response: y
#> Df Sum Sq Mean Sq F value Pr(>F)
#> trat 17 16715382 983258 0.6756 0.80376
#> bloco 2 12998996 6499498 4.4655 0.01897 *
#> Residuals 34 49486177 1455476
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#> # A tibble: 2 × 5
#> designacao rep r rs yp
#> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 11 3 17365. -3.49 20264.
#> 2 17 1 15763. -3.40 18608.
#>
#> NULL
#> [1] "========================"
#> [1] "r Época: 5"
#> [1] "========================"
#> Analysis of Variance Table
#>
#> Response: y
#> Df Sum Sq Mean Sq F value Pr(>F)
#> trat 17 8805500 517971 2.2145 0.02377 *
#> bloco 2 680080 340040 1.4538 0.24784
#> Residuals 34 7952509 233897
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#> # A tibble: 1 × 5
#> designacao rep r rs yp
#> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 14 3 18611 -3.87 19862.
#>
#> NULL
#> [1] "========================"
#> [1] "g Época: 1"
#> [1] "========================"
#> Analysis of Variance Table
#>
#> Response: y
#> Df Sum Sq Mean Sq F value Pr(>F)
#> trat 17 419963521 24703737 4.6480 6.915e-05 ***
#> bloco 2 44315094 22157547 4.1689 0.02403 *
#> Residuals 34 180706732 5314904
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#>
#> NULL
#> [1] "========================"
#> [1] "g Época: 3"
#> [1] "========================"
#> Analysis of Variance Table
#>
#> Response: y
#> Df Sum Sq Mean Sq F value Pr(>F)
#> trat 17 230547651 13561627 0.7708 0.7112
#> bloco 2 74577442 37288721 2.1194 0.1357
#> Residuals 34 598189176 17593799
#>
#> NULL
#> [1] "========================"
#> [1] "g Época: 4"
#> [1] "========================"
#> Analysis of Variance Table
#>
#> Response: y
#> Df Sum Sq Mean Sq F value Pr(>F)
#> trat 17 214544068 12620239 2.2931 0.01933 *
#> bloco 2 634036677 317018338 57.6010 1.205e-11 ***
#> Residuals 34 187125473 5503690
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#> # A tibble: 1 × 5
#> designacao rep g rs yp
#> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 12 1 20955. -3.74 26877.
#>
#> NULL
#> [1] "========================"
#> [1] "g Época: 5"
#> [1] "========================"
#> Analysis of Variance Table
#>
#> Response: y
#> Df Sum Sq Mean Sq F value Pr(>F)
#> trat 17 120513568 7089033 7.1277 6.757e-07 ***
#> bloco 2 54704500 27352250 27.5013 7.858e-08 ***
#> Residuals 34 33815675 994579
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#>
#> NULL
#> [1] "========================"
#> [1] "b Época: 1"
#> [1] "========================"
#> Analysis of Variance Table
#>
#> Response: y
#> Df Sum Sq Mean Sq F value Pr(>F)
#> trat 17 2240.05 131.77 1.8717 0.058792 .
#> bloco 2 841.87 420.94 5.9793 0.005955 **
#> Residuals 34 2393.56 70.40
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#>
#> NULL
#> [1] "========================"
#> [1] "b Época: 3"
#> [1] "========================"
#> Analysis of Variance Table
#>
#> Response: y
#> Df Sum Sq Mean Sq F value Pr(>F)
#> trat 17 1410.71 82.983 2.4141 0.014061 *
#> bloco 2 477.33 238.663 6.9431 0.002962 **
#> Residuals 34 1168.71 34.374
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#>
#> NULL
#> [1] "========================"
#> [1] "b Época: 4"
#> [1] "========================"
#> Analysis of Variance Table
#>
#> Response: y
#> Df Sum Sq Mean Sq F value Pr(>F)
#> trat 17 437.07 25.710 0.8548 0.6253
#> bloco 2 32.39 16.192 0.5383 0.5886
#> Residuals 34 1022.66 30.078
#>
#> NULL
#> [1] "========================"
#> [1] "b Época: 5"
#> [1] "========================"
#> Analysis of Variance Table
#>
#> Response: y
#> Df Sum Sq Mean Sq F value Pr(>F)
#> trat 17 164.538 9.6787 1.6897 0.09462 .
#> bloco 2 37.261 18.6304 3.2525 0.05099 .
#> Residuals 34 194.755 5.7281
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#>
#> NULL
#> [1] "========================"
#> [1] "nir Época: 1"
#> [1] "========================"
#> Analysis of Variance Table
#>
#> Response: y
#> Df Sum Sq Mean Sq F value Pr(>F)
#> trat 17 2002243348 117779020 1.7418 0.08264 .
#> bloco 2 697728463 348864231 5.1592 0.01105 *
#> Residuals 34 2299071503 67619750
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#>
#> NULL
#> [1] "========================"
#> [1] "nir Época: 3"
#> [1] "========================"
#> Analysis of Variance Table
#>
#> Response: y
#> Df Sum Sq Mean Sq F value Pr(>F)
#> trat 17 4.2379e+09 249290192 0.7803 0.7016
#> bloco 2 1.1116e+09 555778907 1.7397 0.1908
#> Residuals 34 1.0862e+10 319471672
#>
#> NULL
#> [1] "========================"
#> [1] "nir Época: 4"
#> [1] "========================"
#> Analysis of Variance Table
#>
#> Response: y
#> Df Sum Sq Mean Sq F value Pr(>F)
#> trat 17 1615335174 95019716 0.8012 0.6803
#> bloco 2 7208514536 3604257268 30.3893 2.698e-08 ***
#> Residuals 34 4032498947 118602910
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#> # A tibble: 1 × 5
#> designacao rep nir rs yp
#> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 14 2 22381. -3.34 47697.
#>
#> NULL
#> [1] "========================"
#> [1] "nir Época: 5"
#> [1] "========================"
#> Analysis of Variance Table
#>
#> Response: y
#> Df Sum Sq Mean Sq F value Pr(>F)
#> trat 17 192219117 11307007 3.5008 0.0009145 ***
#> bloco 2 137588642 68794321 21.2994 1.008e-06 ***
#> Residuals 34 109815848 3229878
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#> # A tibble: 1 × 5
#> designacao rep nir rs yp
#> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 14 1 50314. 4.91 44912.
#>
#> NULL
#> [1] "========================"
#> [1] "red_edge Época: 1"
#> [1] "========================"
#> Analysis of Variance Table
#>
#> Response: y
#> Df Sum Sq Mean Sq F value Pr(>F)
#> trat 17 192002996 11294294 1.7199 0.0874683 .
#> bloco 2 154738631 77369316 11.7822 0.0001296 ***
#> Residuals 34 223265913 6566644
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#>
#> NULL
#> [1] "========================"
#> [1] "red_edge Época: 3"
#> [1] "========================"
#> Analysis of Variance Table
#>
#> Response: y
#> Df Sum Sq Mean Sq F value Pr(>F)
#> trat 17 78862732 4638984 1.0838 0.406185
#> bloco 2 71292735 35646367 8.3280 0.001139 **
#> Residuals 34 145530400 4280306
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#>
#> NULL
#> [1] "========================"
#> [1] "red_edge Época: 4"
#> [1] "========================"
#> Analysis of Variance Table
#>
#> Response: y
#> Df Sum Sq Mean Sq F value Pr(>F)
#> trat 17 47431346 2790079 1.0619 0.425073
#> bloco 2 35541201 17770601 6.7635 0.003366 **
#> Residuals 34 89332457 2627425
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#>
#> NULL
#> [1] "========================"
#> [1] "red_edge Época: 5"
#> [1] "========================"
#> Analysis of Variance Table
#>
#> Response: y
#> Df Sum Sq Mean Sq F value Pr(>F)
#> trat 17 63827976 3754587 1.0038 0.47767
#> bloco 2 24359402 12179701 3.2564 0.05082 .
#> Residuals 34 127166875 3740202
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#> # A tibble: 2 × 5
#> designacao rep red_edge rs yp
#> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 13 1 23867. -3.11 28128.
#> 2 14 3 24213. -3.22 28593.
#>
#> NULL
#> [1] "========================"
#> [1] "ndvi Época: 1"
#> [1] "========================"
#> Analysis of Variance Table
#>
#> Response: y
#> Df Sum Sq Mean Sq F value Pr(>F)
#> trat 17 0.244324 0.014372 1.8589 0.06081 .
#> bloco 2 0.079565 0.039783 5.1455 0.01116 *
#> Residuals 34 0.262872 0.007732
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#> # A tibble: 1 × 5
#> designacao rep ndvi rs yp
#> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 7 2 0.0137 -3.66 0.232
#>
#> NULL
#> [1] "========================"
#> [1] "ndvi Época: 3"
#> [1] "========================"
#> Analysis of Variance Table
#>
#> Response: y
#> Df Sum Sq Mean Sq F value Pr(>F)
#> trat 17 0.60348 0.035499 0.9504 0.5290
#> bloco 2 0.09947 0.049734 1.3314 0.2775
#> Residuals 34 1.27002 0.037354
#>
#> NULL
#> [1] "========================"
#> [1] "ndvi Época: 4"
#> [1] "========================"
#> Analysis of Variance Table
#>
#> Response: y
#> Df Sum Sq Mean Sq F value Pr(>F)
#> trat 17 0.12135 0.00714 0.7509 0.7312
#> bloco 2 0.70167 0.35084 36.9073 3.017e-09 ***
#> Residuals 34 0.32320 0.00951
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#>
#> NULL
#> [1] "========================"
#> [1] "ndvi Época: 5"
#> [1] "========================"
#> Analysis of Variance Table
#>
#> Response: y
#> Df Sum Sq Mean Sq F value Pr(>F)
#> trat 17 0.0119208 0.00070122 2.2269 0.0230085 *
#> bloco 2 0.0062025 0.00310124 9.8489 0.0004226 ***
#> Residuals 34 0.0107060 0.00031488
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#> # A tibble: 1 × 5
#> designacao rep ndvi rs yp
#> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 14 3 0.33 -4.62 0.381
#>
#> NULL
#> [1] "========================"
#> [1] "gndvi Época: 1"
#> [1] "========================"
#> Analysis of Variance Table
#>
#> Response: y
#> Df Sum Sq Mean Sq F value Pr(>F)
#> trat 17 0.127282 0.0074872 2.2241 0.023184 *
#> bloco 2 0.039665 0.0198325 5.8912 0.006357 **
#> Residuals 34 0.114460 0.0033665
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#> # A tibble: 1 × 5
#> designacao rep gndvi rs yp
#> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 7 2 0.0116 -3.74 0.158
#>
#> NULL
#> [1] "========================"
#> [1] "gndvi Época: 3"
#> [1] "========================"
#> Analysis of Variance Table
#>
#> Response: y
#> Df Sum Sq Mean Sq F value Pr(>F)
#> trat 17 0.40734 0.023961 0.9694 0.5104
#> bloco 2 0.06240 0.031198 1.2622 0.2959
#> Residuals 34 0.84037 0.024717
#>
#> NULL
#> [1] "========================"
#> [1] "gndvi Época: 4"
#> [1] "========================"
#> Analysis of Variance Table
#>
#> Response: y
#> Df Sum Sq Mean Sq F value Pr(>F)
#> trat 17 0.20303 0.011943 1.1162 0.3792
#> bloco 2 0.36018 0.180091 16.8316 8.301e-06 ***
#> Residuals 34 0.36378 0.010700
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#>
#> NULL
#> [1] "========================"
#> [1] "gndvi Época: 5"
#> [1] "========================"
#> Analysis of Variance Table
#>
#> Response: y
#> Df Sum Sq Mean Sq F value Pr(>F)
#> trat 17 0.061134 0.0035961 7.4827 3.797e-07 ***
#> bloco 2 0.000905 0.0004524 0.9413 0.4001
#> Residuals 34 0.016340 0.0004806
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#> # A tibble: 1 × 5
#> designacao rep gndvi rs yp
#> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 14 1 0.332 4.13 0.273
#>
#> NULL
#> [1] "========================"
#> [1] "savi Época: 1"
#> [1] "========================"
#> Analysis of Variance Table
#>
#> Response: y
#> Df Sum Sq Mean Sq F value Pr(>F)
#> trat 17 0.60489 0.035582 1.7927 0.07235 .
#> bloco 2 0.12407 0.062033 3.1253 0.05675 .
#> Residuals 34 0.67484 0.019848
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#> # A tibble: 2 × 5
#> designacao rep savi rs yp
#> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 7 2 0.0206 -3.44 0.356
#> 2 9 2 0.727 3.25 0.406
#>
#> NULL
#> [1] "========================"
#> [1] "savi Época: 3"
#> [1] "========================"
#> Analysis of Variance Table
#>
#> Response: y
#> Df Sum Sq Mean Sq F value Pr(>F)
#> trat 17 1.32159 0.077740 0.8982 0.5810
#> bloco 2 0.21803 0.109017 1.2595 0.2967
#> Residuals 34 2.94282 0.086553
#>
#> NULL
#> [1] "========================"
#> [1] "savi Época: 4"
#> [1] "========================"
#> Analysis of Variance Table
#>
#> Response: y
#> Df Sum Sq Mean Sq F value Pr(>F)
#> trat 17 0.56635 0.03331 0.9672 0.5126
#> bloco 2 1.34900 0.67450 19.5821 2.198e-06 ***
#> Residuals 34 1.17112 0.03444
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#> # A tibble: 1 × 5
#> designacao rep savi rs yp
#> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 14 2 0.0903 -3.14 0.502
#>
#> NULL
#> [1] "========================"
#> [1] "savi Época: 5"
#> [1] "========================"
#> Analysis of Variance Table
#>
#> Response: y
#> Df Sum Sq Mean Sq F value Pr(>F)
#> trat 17 0.026821 0.0015777 2.2270 0.0230071 *
#> bloco 2 0.013955 0.0069777 9.8491 0.0004225 ***
#> Residuals 34 0.024088 0.0007085
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#> # A tibble: 1 × 5
#> designacao rep savi rs yp
#> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 14 3 0.495 -4.62 0.572
#>
#> NULL
#> [1] "========================"
#> [1] "osavi Época: 1"
#> [1] "========================"
#> Analysis of Variance Table
#>
#> Response: y
#> Df Sum Sq Mean Sq F value Pr(>F)
#> trat 17 0.38755 0.022797 1.4847 0.1596
#> bloco 2 0.04750 0.023751 1.5468 0.2275
#> Residuals 34 0.52205 0.015354
#> # A tibble: 1 × 5
#> designacao rep osavi rs yp
#> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 1 1 0.0358 -4.36 0.383
#>
#> NULL
#> [1] "========================"
#> [1] "osavi Época: 3"
#> [1] "========================"
#> Analysis of Variance Table
#>
#> Response: y
#> Df Sum Sq Mean Sq F value Pr(>F)
#> trat 17 0.87218 0.051305 1.1303 0.3679
#> bloco 2 0.13411 0.067056 1.4773 0.2425
#> Residuals 34 1.54329 0.045391
#>
#> NULL
#> [1] "========================"
#> [1] "osavi Época: 4"
#> [1] "========================"
#> Analysis of Variance Table
#>
#> Response: y
#> Df Sum Sq Mean Sq F value Pr(>F)
#> trat 17 0.22723 0.01337 0.6085 0.8616
#> bloco 2 0.82141 0.41070 18.6961 3.335e-06 ***
#> Residuals 34 0.74689 0.02197
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#> # A tibble: 2 × 5
#> designacao rep osavi rs yp
#> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 11 3 0.156 -3.23 0.492
#> 2 14 2 0.0698 -3.32 0.413
#>
#> NULL
#> [1] "========================"
#> [1] "osavi Época: 5"
#> [1] "========================"
#> Analysis of Variance Table
#>
#> Response: y
#> Df Sum Sq Mean Sq F value Pr(>F)
#> trat 17 0.036633 0.0021549 1.4623 0.16880
#> bloco 2 0.014884 0.0074418 5.0499 0.01201 *
#> Residuals 34 0.050104 0.0014736
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#> # A tibble: 1 × 5
#> designacao rep osavi rs yp
#> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 14 3 0.221 -12.5 0.383
#>
#> NULL
#> [1] "========================"
#> [1] "ndre Época: 1"
#> [1] "========================"
#> Analysis of Variance Table
#>
#> Response: y
#> Df Sum Sq Mean Sq F value Pr(>F)
#> trat 17 0.28205 0.016591 1.1881 0.323916
#> bloco 2 0.17577 0.087884 6.2937 0.004727 **
#> Residuals 34 0.47477 0.013964
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#> # A tibble: 1 × 5
#> designacao rep ndre rs yp
#> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 1 1 -0.0196 -3.66 0.274
#>
#> NULL
#> [1] "========================"
#> [1] "ndre Época: 3"
#> [1] "========================"
#> Analysis of Variance Table
#>
#> Response: y
#> Df Sum Sq Mean Sq F value Pr(>F)
#> trat 17 0.85255 0.050150 1.0579 0.4286
#> bloco 2 0.08050 0.040252 0.8491 0.4367
#> Residuals 34 1.61183 0.047407
#>
#> NULL
#> [1] "========================"
#> [1] "ndre Época: 4"
#> [1] "========================"
#> Analysis of Variance Table
#>
#> Response: y
#> Df Sum Sq Mean Sq F value Pr(>F)
#> trat 17 0.28487 0.01676 0.5409 0.9106
#> bloco 2 0.81468 0.40734 13.1499 5.885e-05 ***
#> Residuals 34 1.05321 0.03098
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#> # A tibble: 1 × 5
#> designacao rep ndre rs yp
#> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 11 3 -0.180 -3.31 0.227
#>
#> NULL
#> [1] "========================"
#> [1] "ndre Época: 5"
#> [1] "========================"
#> Analysis of Variance Table
#>
#> Response: y
#> Df Sum Sq Mean Sq F value Pr(>F)
#> trat 17 0.048269 0.0028394 1.1634 0.3422224
#> bloco 2 0.055013 0.0275064 11.2705 0.0001758 ***
#> Residuals 34 0.082979 0.0024406
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#> # A tibble: 1 × 5
#> designacao rep ndre rs yp
#> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 14 3 -0.0561 -5.96 0.108
#>
#> NULL
#> [1] "========================"
#> [1] "vari Época: 1"
#> [1] "========================"
#> Analysis of Variance Table
#>
#> Response: y
#> Df Sum Sq Mean Sq F value Pr(>F)
#> trat 17 0.088754 0.0052208 3.4681 0.0009889 ***
#> bloco 2 0.008653 0.0043264 2.8739 0.0702758 .
#> Residuals 34 0.051184 0.0015054
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#> # A tibble: 1 × 5
#> designacao rep vari rs yp
#> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 7 2 0.00147 -3.02 0.0850
#>
#> NULL
#> [1] "========================"
#> [1] "vari Época: 3"
#> [1] "========================"
#> Analysis of Variance Table
#>
#> Response: y
#> Df Sum Sq Mean Sq F value Pr(>F)
#> trat 17 0.101124 0.0059485 1.0934 0.3981
#> bloco 2 0.016702 0.0083508 1.5350 0.2300
#> Residuals 34 0.184975 0.0054404
#>
#> NULL
#> [1] "========================"
#> [1] "vari Época: 4"
#> [1] "========================"
#> Analysis of Variance Table
#>
#> Response: y
#> Df Sum Sq Mean Sq F value Pr(>F)
#> trat 17 0.086638 0.0050964 3.7624 0.0004934 ***
#> bloco 2 0.061210 0.0306052 22.5946 5.726e-07 ***
#> Residuals 34 0.046054 0.0013545
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#> # A tibble: 1 × 5
#> designacao rep vari rs yp
#> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 13 1 0.190 3.25 0.106
#>
#> NULL
#> [1] "========================"
#> [1] "vari Época: 5"
#> [1] "========================"
#> Analysis of Variance Table
#>
#> Response: y
#> Df Sum Sq Mean Sq F value Pr(>F)
#> trat 17 0.042047 0.0024733 9.3999 2.253e-08 ***
#> bloco 2 0.007475 0.0037376 14.2048 3.280e-05 ***
#> Residuals 34 0.008946 0.0002631
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#>
#> NULL